The aggregated model for human target classification

Target classification is a common task in computer vision, aiming to assign the contents of an image to specified categories, representing one of the most significant research areas in the field. Involving various targets, human body classification serves as a prerequisite task for human body detection, and accurate classification results play a crucial role in this task. Despite many studies aimed at improving classification accuracy, there is still room for improvement. We propose aggregating the results of multiple classifiers for analysis and further learning on the classification results to enhance accuracy. Our research demonstrates that aggregating the results of multiple classifiers for further learning yields classification results superior to any single classifier, suggesting a promising direction for improving classification accuracy for further investigation.